Spatial Data Reduction

Spatial Data Reduction is a spatial data processing technique that reduces the volume or complexity of spatial datasets while preserving essential geographic patterns, structures, or analytical utility, distinguishing it from general data reduction methods by explicitly accounting for spatial autocorrelation, scale, and topology.

57

Publications

2.4K

Citations

230

Authors

116

Institutions

Publications per year

2017–2026

23

Authors

230

  1. GT
    Goce Trajcevski

    Iowa State University, United States

    2 publications · 233 citations

    2

    h-index

  2. MW
    Michael Whelan

    University College Dublin, Ireland

    2 publications · 26 citations

    2

    h-index

  3. OW
    Ouri Wolfson

    University of Illinois Chicago, United States

    2 publications · 233 citations

    2

    h-index

  4. HC
    Hu Cao

    Chinese Academy of Sciences, China

    2 publications · 233 citations

    2

    h-index

  5. HC
    Huizhong Chen

    National Center for Toxicological Research, United States

    1 publications · 12 citations

    1

    h-index

1–5 of 230

Venues

  1. 3

    h-index

  2. Applied Spectroscopy

    2 publications · 41 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 24 citations

    2

    h-index

  4. 2

    h-index

  5. Journal of Geophysical Research Atmospheres

    JGR · 2 publications · 95 citations

    2

    h-index

1–5